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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/meituan/YOLOv6/blob/main/turtorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "This is the official YOLOv6 notebook by MeiTuan, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n",
        "For more information please visit https://github.com/meituan/YOLOv6. Thank you!"
      ],
      "metadata": {
        "id": "8gBC7pTzB_ds"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Introduction\n",
        "\n",
        "YOLOv6 is a single-stage object detection framework dedicated to industrial applications, with hardware-friendly efficient design and high performance.\n",
        "\n",
        "YOLOv6-nano achieves 35.0 mAP on COCO val2017 dataset with 1242 FPS on T4 using TensorRT FP16 for bs32 inference, and YOLOv6-s achieves 43.1 mAP on COCO val2017 dataset with 520 FPS on T4 using TensorRT FP16 for bs32 inference.\n",
        "\n",
        "YOLOv6 is composed of the following methods:\n",
        "\n",
        "Hardware-friendly Design for Backbone and Neck\n",
        "Efficient Decoupled Head with SIoU Loss"
      ],
      "metadata": {
        "id": "fZVZhl5uCHka"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup\n",
        "Clone repo and install dependencies."
      ],
      "metadata": {
        "id": "lmqEfutmVHjs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/meituan/YOLOv6.git\n",
        "%cd YOLOv6\n",
        "%pip install -r requirements.txt"
      ],
      "metadata": {
        "id": "XA52vLvX06io",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44505140-dd14-45f5-ccef-9deb599a1015"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'YOLOv6'...\n",
            "remote: Enumerating objects: 1807, done.\u001b[K\n",
            "remote: Counting objects: 100% (821/821), done.\u001b[K\n",
            "remote: Compressing objects: 100% (226/226), done.\u001b[K\n",
            "remote: Total 1807 (delta 630), reused 697 (delta 592), pack-reused 986\u001b[K\n",
            "Receiving objects: 100% (1807/1807), 16.60 MiB | 5.25 MiB/s, done.\n",
            "Resolving deltas: 100% (994/994), done.\n",
            "/content/YOLOv6\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (1.12.1+cu113)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (0.13.1+cu113)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (1.21.6)\n",
            "Requirement already satisfied: opencv-python>=4.1.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (4.6.0.66)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 8)) (6.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 9)) (1.7.3)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 10)) (4.64.0)\n",
            "Collecting addict>=2.4.0\n",
            "  Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
            "Requirement already satisfied: tensorboard>=2.7.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 12)) (2.8.0)\n",
            "Requirement already satisfied: pycocotools>=2.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 13)) (2.0.4)\n",
            "Collecting onnx>=1.10.0\n",
            "  Downloading onnx-1.12.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 13.1 MB 21.0 MB/s \n",
            "\u001b[?25hCollecting onnx-simplifier>=0.3.6\n",
            "  Downloading onnx_simplifier-0.4.8-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0 MB 43.5 MB/s \n",
            "\u001b[?25hCollecting thop\n",
            "  Downloading thop-0.1.1.post2207130030-py3-none-any.whl (15 kB)\n",
            "\u001b[31mERROR: Could not find a version that satisfies the requirement pytorch_quantization>=2.1.1 (from versions: 0.0.1.dev4, 0.0.1.dev5)\u001b[0m\n",
            "\u001b[31mERROR: No matching distribution found for pytorch_quantization>=2.1.1\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Inference\n",
        "First, download a pretrained model from the YOLOv6 [release](https://github.com/meituan/YOLOv6/releases).\n",
        "\n"
      ],
      "metadata": {
        "id": "dTJcbev3VO6i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download a pretrained model\n",
        "import torch\n",
        "torch.hub.download_url_to_file('https://github.com/meituan/YOLOv6/releases/download/0.2.0/yolov6s.pt', 'yolov6s.pt')"
      ],
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        "outputId": "61d070d8-2cfe-4bc4-b998-af581c18296e"
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      "execution_count": null,
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        {
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            "text/plain": [
              "  0%|          | 0.00/36.3M [00:00<?, ?B/s]"
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    {
      "cell_type": "markdown",
      "source": [
        "Second, run inference with `tools/infer.py`, and saving results to `runs/inference`. Example inference sources are:\n",
        "\n",
        "```shell\n",
        "python tools/infer.py --weights yolov6s.pt --source img.jpg / imgdir\n",
        "                                yolov6n.pt\n",
        "```"
      ],
      "metadata": {
        "id": "qXrHMtniHtBG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python tools/infer.py --weights yolov6s.pt --source data/images/image1.jpg\n",
        "# show image\n",
        "from google.colab.patches import cv2_imshow, cv2\n",
        "img = cv2.imread('runs/inference/image1.jpg')\n",
        "cv2_imshow(img)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "9LUHrwwrWglt",
        "outputId": "fac2f29b-7a9b-4e45-cc15-b36b09dbe148"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(agnostic_nms=False, classes=None, conf_thres=0.4, device='0', half=False, hide_conf=False, hide_labels=False, img_size=640, iou_thres=0.45, max_det=1000, name='exp', project='runs/inference', save_dir=None, save_img=True, save_txt=False, source='data/images/image1.jpg', view_img=False, weights='yolov6s.pt', yaml='data/coco.yaml')\n",
            "Loading checkpoint from yolov6s.pt\n",
            "\n",
            "Fusing model...\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Switch model to deploy modality.\n",
            "100% 1/1 [00:00<00:00,  6.76it/s]\n",
            "Results saved to runs/inference/exp\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=640x536 at 0x7EFF9F2B2D50>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Validate\n",
        "Validate a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. Models are downloaded automatically from the [latest YOLOv6 release](https://github.com/meituan/YOLOv6/releases). "
      ],
      "metadata": {
        "id": "WoGnT0H5aGfL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## COCO val\n",
        "Download COCO val 2017 dataset (1GB - 5000 images), and test model accuracy."
      ],
      "metadata": {
        "id": "hFldWvR9aWUx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download COCO val\n",
        "import torch\n",
        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../ && rm tmp.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "8cc18eb575624cc8a3a2c235e9705d6c",
            "9ad7290d97ea44488747aae321677109",
            "2cdd2bb7d83b4f9db87ae900bdcf342d",
            "2e5401125b7248e3a081e3f57a571064",
            "74afade5e66049249b04ccdf99ac5bc3",
            "9eee5d30302b45a7b172846c58c18782",
            "84d9721bb8bd41819f5f02fdd808f35c",
            "703c9ed6ee7446d3a8efc3e20ece6dca",
            "51b7fa10e42349998974eba12fa06458",
            "4cedeada524642d6ac56a8d383ecf1ce",
            "6875d51042d54c978a81048a43268dbb"
          ]
        },
        "id": "l2SdQABjYvs6",
        "outputId": "58a26eef-e359-4a30-8e53-70fc6ef99a30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/780M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8cc18eb575624cc8a3a2c235e9705d6c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run yolov6x on coco val\n",
        "!python tools/eval.py --weights yolov6s.pt --data data/coco.yaml --img 640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nNDGzITd2Ys1",
        "outputId": "6e079132-b313-4f48-9b7f-6fbbe24bf6c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(batch_size=32, conf_thres=0.001, data='data/coco.yaml', device='0', half=False, img_size=640, iou_thres=0.65, name='exp', save_dir='runs/val/', task='val', weights='yolov6s.pt')\n",
            "Loading checkpoint from yolov6s.pt\n",
            "\n",
            "Fusing model...\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Switch model to deploy modality.\n",
            "Model Summary: Params: 17.22M, Gflops: 44.19\n",
            "Val: Checking formats of images with 2 process(es): \n",
            "0 image(s) corrupted: 100% 5000/5000 [00:00<00:00, 6175.56it/s]\n",
            "Val: Checking formats of labels with 2 process(es): \n",
            "4952 label(s) found, 48 label(s) missing, 0 label(s) empty, 0 invalid label files: 100% 5000/5000 [00:01<00:00, 4264.74it/s]\n",
            "Val: Final numbers of valid images: 5000/ labels: 5000. \n",
            "2.7s for dataset initialization.\n",
            "Inferencing model in val datasets.: 100% 157/157 [01:21<00:00,  1.92it/s]\n",
            "\n",
            "Evaluating speed.\n",
            "Average pre-process time: 0.14 ms\n",
            "Average inference time: 5.91 ms\n",
            "Average NMS time: 1.18 ms\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/val/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.40s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=4.36s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=62.64s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=11.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.431\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.620\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.462\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.239\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.474\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.588\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.346\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.557\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.601\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.402\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.655\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764\n",
            "Results saved to runs/val/exp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train coco data\n",
        "conf: select config file to specify network/optimizer/hyperparameters\n",
        "\n",
        "data: prepare [COCO](http://cocodataset.org)  dataset, [YOLO format coco labes](https://github.com/meituan/YOLOv6/releases/download/0.1.0/coco2017labels.zip) and specify dataset paths in data.yaml\n",
        "\n",
        "make sure your dataset structure as fellows:\n",
        "```shell\n",
        "├── coco\n",
        "│   ├── annotations\n",
        "│   │   ├── instances_train2017.json\n",
        "│   │   └── instances_val2017.json\n",
        "│   ├── images\n",
        "│   │   ├── train2017\n",
        "│   │   └── val2017\n",
        "│   ├── labels\n",
        "│   │   ├── train2017\n",
        "│   │   ├── val2017\n",
        "│   ├── LICENSE\n",
        "│   ├── README.txt\n",
        "```"
      ],
      "metadata": {
        "id": "bO6NirzdeITA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## COCO datasets"
      ],
      "metadata": {
        "id": "Hidcp3AXuCkV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download coco datasets and need about 30mins.\n",
        "%cd ..\n",
        "%cd coco/images\n",
        "!wget http://images.cocodataset.org/zips/train2017.zip\n",
        "!wget http://images.cocodataset.org/zips/val2017.zip\n",
        "!wget http://images.cocodataset.org/zips/test2017.zip\n",
        "!unzip train2017.zip && rm train2017.zip\n",
        "!unzip val2017.zip && rm val2017.zip\n",
        "!unzip test2017.zip && rm test2017.zip"
      ],
      "metadata": {
        "id": "p4N3Bv84qbkP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Before running, you need to make sure you're in the YOLOv6 root directory.\n",
        "# Train YOLOv6s on COCO for 30 epochs\n",
        "!python tools/train.py --img 640 --batch 32 --epochs 30 --conf configs/yolov6s.py --data data/coco.yaml"
      ],
      "metadata": {
        "id": "JpcC_L6whcxW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## COCO128 datasets\n",
        "You need create a new file `coco128.yaml` under the folder `./data`.The details are as follows:\n",
        "\n",
        "```\n",
        "# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]\n",
        "path: ../coco128  # dataset root dir\n",
        "train: images/train2017  # train images (relative to 'path') 128 images\n",
        "val: images/train2017  # val images (relative to 'path') 128 images\n",
        "test:  # test images (optional)\n",
        "\n",
        "# Classes\n",
        "nc: 80  # number of classes\n",
        "names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',\n",
        "        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',\n",
        "        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',\n",
        "        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',\n",
        "        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',\n",
        "        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',\n",
        "        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',\n",
        "        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',\n",
        "        'hair drier', 'toothbrush']  # class names\n",
        "```"
      ],
      "metadata": {
        "id": "az4pa71UuObL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download coco128 datasets\n",
        "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco128.zip', 'tmp.zip')\n",
        "!unzip -q tmp.zip -d ../ && rm tmp.zip\n",
        "\n",
        "# torch.hub.download_url_to_file('https://drive.google.com/file/d/1HICm-rrsdp89GNpFbzcwksHRtDx10McK/view?usp=sharing', 'tmp.zip')\n",
        "# !unzip -q tmp.zip -d ../ && rm tmp.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "11c01641cf274e118221dbbdc2f3afa4",
            "693b2ece47844511905ca94736c11fdd",
            "2adc2664afd5404da77a1bfede169b95",
            "7566d05f122f4281825838145d488860",
            "7b1052fd864b4e8da4941a8647ef620b",
            "fe4d2e0d47604eb08e7f4743dc0e6826",
            "5baa548a06294dd4a4a8a3330189f4cd",
            "2888af90fe40440f9f03fdcafd49da75",
            "d976baa0f3374eb4b5278e10eced2dfc",
            "11378927dc5e440080fd0300e5c301ed",
            "4cafdf53ddcc415680777bbf54399064"
          ]
        },
        "id": "qQAhslIXjjGX",
        "outputId": "5be3f8bd-1f80-4844-ee0e-aeb212ea5d35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/6.66M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "11c01641cf274e118221dbbdc2f3afa4"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train YOLOv6s on COCO128 for 100 epochs\n",
        "!python tools/train.py --img 640 --batch 32 --epochs 100 --data data/coco128.yaml"
      ],
      "metadata": {
        "id": "zHfmsqelioX_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6912bd42-2fcb-4b53-9b89-2efc77b38b91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using 1 GPU for training... \n",
            "training args are: Namespace(batch_size=32, check_images=False, check_labels=False, conf_file='./configs/yolov6s.py', data_path='data/coco128.yaml', device='0', dist_url='env://', epochs=100, eval_final_only=False, eval_interval=20, gpu_count=0, heavy_eval_range=50, img_size=640, local_rank=-1, name='exp', output_dir='./runs/train', rank=-1, resume=False, save_dir='runs/train/exp', workers=8, world_size=1)\n",
            "\n",
            "Train: Checking formats of images with 2 process(es): \n",
            "\r  0% 0/128 [00:00<?, ?it/s]\r0 image(s) corrupted: 100% 128/128 [00:00<00:00, 3223.54it/s]\n",
            "Train: Checking formats of labels with 2 process(es): \n",
            "128 label(s) found, 0 label(s) missing, 2 label(s) empty, 0 invalid label files: 100% 128/128 [00:00<00:00, 3653.13it/s]\n",
            "Train: Final numbers of valid images: 128/ labels: 128. \n",
            "0.2s for dataset initialization.\n",
            "Convert to COCO format\n",
            "100% 128/128 [00:00<00:00, 32588.98it/s]\n",
            "Convert to COCO format finished. Resutls saved in ../coco128/annotations/instances_train2017.json\n",
            "Val: Final numbers of valid images: 128/ labels: 128. \n",
            "0.1s for dataset initialization.\n",
            "Model: Model(\n",
            "  (backbone): EfficientRep(\n",
            "    (stem): RepVGGBlock(\n",
            "      (nonlinearity): ReLU(inplace=True)\n",
            "      (se): Identity()\n",
            "      (rbr_dense): Sequential(\n",
            "        (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      )\n",
            "      (rbr_1x1): Sequential(\n",
            "        (conv): Conv2d(3, 32, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "        (bn): BatchNorm2d(32, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_2): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(32, 64, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_3): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (2): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_4): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (2): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (3): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (4): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (ERBlock_5): Sequential(\n",
            "      (0): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (1): RepBlock(\n",
            "        (conv1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (block): Sequential(\n",
            "          (0): RepVGGBlock(\n",
            "            (nonlinearity): ReLU(inplace=True)\n",
            "            (se): Identity()\n",
            "            (rbr_identity): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            (rbr_dense): Sequential(\n",
            "              (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "            (rbr_1x1): Sequential(\n",
            "              (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (2): SimSPPF(\n",
            "        (cv1): SimConv(\n",
            "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (act): ReLU(inplace=True)\n",
            "        )\n",
            "        (cv2): SimConv(\n",
            "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(512, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (act): ReLU(inplace=True)\n",
            "        )\n",
            "        (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (neck): RepPANNeck(\n",
            "    (Rep_p4): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(384, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(384, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_p3): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(192, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_n3): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (Rep_n4): RepBlock(\n",
            "      (conv1): RepVGGBlock(\n",
            "        (nonlinearity): ReLU(inplace=True)\n",
            "        (se): Identity()\n",
            "        (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (rbr_dense): Sequential(\n",
            "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "        (rbr_1x1): Sequential(\n",
            "          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "          (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        )\n",
            "      )\n",
            "      (block): Sequential(\n",
            "        (0): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (1): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "        (2): RepVGGBlock(\n",
            "          (nonlinearity): ReLU(inplace=True)\n",
            "          (se): Identity()\n",
            "          (rbr_identity): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          (rbr_dense): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "          (rbr_1x1): Sequential(\n",
            "            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "            (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (reduce_layer0): SimConv(\n",
            "      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (upsample0): Transpose(\n",
            "      (upsample_transpose): ConvTranspose2d(128, 128, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "    (reduce_layer1): SimConv(\n",
            "      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (upsample1): Transpose(\n",
            "      (upsample_transpose): ConvTranspose2d(64, 64, kernel_size=(2, 2), stride=(2, 2))\n",
            "    )\n",
            "    (downsample2): SimConv(\n",
            "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "    (downsample1): SimConv(\n",
            "      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
            "      (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "      (act): ReLU(inplace=True)\n",
            "    )\n",
            "  )\n",
            "  (detect): Detect(\n",
            "    (cls_convs): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (reg_convs): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "    (cls_preds): ModuleList(\n",
            "      (0): Conv2d(64, 80, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): Conv2d(128, 80, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): Conv2d(256, 80, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (reg_preds): ModuleList(\n",
            "      (0): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): Conv2d(128, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): Conv2d(256, 4, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (obj_preds): ModuleList(\n",
            "      (0): Conv2d(64, 1, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (1): Conv2d(128, 1, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (2): Conv2d(256, 1, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (stems): ModuleList(\n",
            "      (0): Conv(\n",
            "        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(64, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (1): Conv(\n",
            "        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(128, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "      (2): Conv(\n",
            "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "        (bn): BatchNorm2d(256, eps=0.001, momentum=0.03, affine=True, track_running_stats=True)\n",
            "        (act): SiLU(inplace=True)\n",
            "      )\n",
            "    )\n",
            "  )\n",
            ")\n",
            "Training start...\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "  0% 0/4 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "      0/99     4.583     3.153     11.44     1.808: 100% 4/4 [00:13<00:00,  3.28s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:01<00:00,  1.04it/s]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Epoch: 0 | mAP@0.5: 0.0 | mAP@0.50:0.95: 0.0\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      1/99     3.988     2.417     7.039     2.213: 100% 4/4 [00:03<00:00,  1.00it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      2/99     3.327     2.062     5.917     2.692: 100% 4/4 [00:03<00:00,  1.00it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      3/99     3.096      2.05     5.933     2.913: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      4/99     3.036      2.09     6.123     2.944: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      5/99     2.945     2.141     6.142     3.026: 100% 4/4 [00:03<00:00,  1.03it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      6/99     2.981     2.144     6.194     3.015: 100% 4/4 [00:03<00:00,  1.03it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      7/99     2.928     2.085     6.052     2.984: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      8/99     2.989     2.122     5.974     2.967: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "      9/99     2.916     2.101     5.909     2.979: 100% 4/4 [00:03<00:00,  1.04it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     10/99     2.977     2.141     5.939     2.909: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     11/99     3.052     2.128     5.857     2.882: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     12/99     2.887      2.08     5.903     2.938: 100% 4/4 [00:04<00:00,  1.00s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     13/99     2.914     2.188      5.94     2.902: 100% 4/4 [00:03<00:00,  1.05it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     14/99     2.872     2.132     5.783     2.912: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     15/99     2.903     2.114     5.846     2.891: 100% 4/4 [00:03<00:00,  1.04it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     16/99     2.902     2.148     5.964     2.892: 100% 4/4 [00:03<00:00,  1.03it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     17/99     2.897     2.128     5.845     2.856: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     18/99     2.904     2.119     5.845     2.814: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     19/99     2.948     2.135     5.832     2.822: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     20/99     2.977     2.138     5.777     2.792: 100% 4/4 [00:03<00:00,  1.00it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:02<00:00,  1.07s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.14s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.27s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            "Epoch: 20 | mAP@0.5: 1.6750818591566045e-06 | mAP@0.50:0.95: 3.3892356037839674e-07\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     21/99     2.983     2.114     5.881     2.747: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     22/99     2.968     2.096     5.737     2.809: 100% 4/4 [00:03<00:00,  1.00it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     23/99     2.983     2.115     5.701     2.802: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     24/99     2.917     2.066     5.781       2.8: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     25/99     2.861     2.095     5.682     2.829: 100% 4/4 [00:03<00:00,  1.00it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     26/99      2.97     2.137     5.713     2.772: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     27/99     3.011     2.111     5.732     2.768: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     28/99     2.951     2.126     5.782     2.765: 100% 4/4 [00:03<00:00,  1.03it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     29/99     2.896     2.122     5.653     2.742: 100% 4/4 [00:03<00:00,  1.04it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     30/99     2.924     2.167     5.641      2.77: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     31/99     2.933     2.152     5.604     2.773: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     32/99     2.849     2.098      5.67     2.849: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     33/99     2.939      2.15     5.628     2.768: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     34/99      2.92     2.104     5.545     2.721: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     35/99     2.933     2.129     5.512     2.737: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     36/99     2.918     2.073     5.513      2.74: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     37/99     3.028     2.135     5.644     2.779: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     38/99     2.993     2.112     5.544     2.738: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     39/99     2.926     2.106     5.491     2.759: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     40/99     2.934     2.109     5.577     2.799: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:03<00:00,  1.95s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.22s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.82s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.37s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n",
            "Epoch: 40 | mAP@0.5: 3.026791640822253e-05 | mAP@0.50:0.95: 6.039800579863439e-06\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     41/99     2.914     2.091     5.657     2.752: 100% 4/4 [00:04<00:00,  1.00s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     42/99     2.838     2.117     5.409     2.809: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     43/99     2.876     2.136     5.486      2.79: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     44/99     2.885     2.117     5.537     2.808: 100% 4/4 [00:03<00:00,  1.00it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     45/99     2.853     2.126     5.489     2.687: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     46/99     2.899     2.088     5.509     2.721: 100% 4/4 [00:03<00:00,  1.04it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     47/99     2.898     2.131      5.45     2.737: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     48/99     2.906     2.083     5.451     2.764: 100% 4/4 [00:03<00:00,  1.02it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     49/99     2.821     2.041     5.355     2.744: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     50/99     2.965     2.119     5.578     2.727: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.02s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.93s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.012\n",
            "Epoch: 50 | mAP@0.5: 0.0011824408042347809 | mAP@0.50:0.95: 0.0006655534080554066\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     51/99     2.851     2.088     5.553     2.752: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.00s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.23s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.10s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.015\n",
            "Epoch: 51 | mAP@0.5: 0.0009480009060269342 | mAP@0.50:0.95: 0.00032895331614389605\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     52/99     2.871     2.043     5.434     2.804: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:03<00:00,  1.98s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.10s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 52 | mAP@0.5: 0.0031166655823601013 | mAP@0.50:0.95: 0.001677834178007029\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     53/99      2.88     2.068     5.504     2.734: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:03<00:00,  1.99s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.09s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.013\n",
            "Epoch: 53 | mAP@0.5: 0.0022269664066922627 | mAP@0.50:0.95: 0.0011167281467850919\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     54/99     2.888     2.019     5.376     2.783: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.02s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.23s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.09s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 54 | mAP@0.5: 0.0010945365556061782 | mAP@0.50:0.95: 0.0002490448763870229\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     55/99      2.97     2.088     5.397     2.722: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.10s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.08s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.005\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.013\n",
            "Epoch: 55 | mAP@0.5: 0.005153651332037936 | mAP@0.50:0.95: 0.0016851377773941941\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     56/99     2.882     2.083     5.334     2.711: 100% 4/4 [00:04<00:00,  1.07s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.10s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.40s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.94s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 56 | mAP@0.5: 0.0009605207151018805 | mAP@0.50:0.95: 0.00022679096614965465\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     57/99      2.82     2.024     5.357     2.727: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.18s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.92s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.004\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 57 | mAP@0.5: 0.0036119729632591684 | mAP@0.50:0.95: 0.0008314271402840496\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     58/99     2.812     2.039     5.328     2.699: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.11s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.26s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.91s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.42s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n",
            "Epoch: 58 | mAP@0.5: 0.001963490873583836 | mAP@0.50:0.95: 0.0004543273581016357\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     59/99     2.778     2.002     5.236     2.708: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.12s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.87s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n",
            "Epoch: 59 | mAP@0.5: 0.0010819224987984467 | mAP@0.50:0.95: 0.000243906737345905\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     60/99     2.854     2.046     5.339     2.733: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.10s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.03s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n",
            "Epoch: 60 | mAP@0.5: 0.0006596587362550893 | mAP@0.50:0.95: 0.00023252221958629067\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     61/99     2.809     2.055     5.321     2.677: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.13s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.23s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.07s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n",
            "Epoch: 61 | mAP@0.5: 0.0004019507666707206 | mAP@0.50:0.95: 9.430218270547424e-05\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     62/99     2.788      2.07     5.374     2.751: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.07s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.40s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.86s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n",
            "Epoch: 62 | mAP@0.5: 0.00035239314273155113 | mAP@0.50:0.95: 0.00011626138925332593\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     63/99     2.817     2.013     5.315     2.706: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.16s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.26s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.88s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.37s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.004\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 63 | mAP@0.5: 0.004203667127063244 | mAP@0.50:0.95: 0.0012639422635148776\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     64/99     2.817     2.014      5.31     2.755: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.09s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.02s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.005\n",
            "Epoch: 64 | mAP@0.5: 0.0008575287529128892 | mAP@0.50:0.95: 0.000191750774372226\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     65/99      2.83     2.015     5.352      2.78: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.02s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.03s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.37s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n",
            "Epoch: 65 | mAP@0.5: 0.0017627231047534784 | mAP@0.50:0.95: 0.0012750007112268785\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     66/99     2.837     2.025     5.314     2.778: 100% 4/4 [00:03<00:00,  1.00it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.12s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.41s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.93s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.013\n",
            "Epoch: 66 | mAP@0.5: 0.0033239425757449724 | mAP@0.50:0.95: 0.002065487064492566\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     67/99     2.813     2.015     5.284     2.759: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.22s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.94s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.005\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.011\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.016\n",
            "Epoch: 67 | mAP@0.5: 0.004636293584747839 | mAP@0.50:0.95: 0.003096991523250025\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     68/99     2.794     2.029     5.313     2.773: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.05s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.92s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 68 | mAP@0.5: 0.0012790427934887515 | mAP@0.50:0.95: 0.0004166938502445351\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     69/99     2.797     2.022     5.254     2.751: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.26s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.06s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.007\n",
            "Epoch: 69 | mAP@0.5: 0.002834136481615038 | mAP@0.50:0.95: 0.0005070867255685134\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     70/99     2.818      2.02     5.277     2.751: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.14s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.06s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n",
            "Epoch: 70 | mAP@0.5: 0.0032584690726534687 | mAP@0.50:0.95: 0.0010810245310215571\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     71/99     2.775      2.01     5.242      2.76: 100% 4/4 [00:04<00:00,  1.04s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.07s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.42s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.86s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n",
            "Epoch: 71 | mAP@0.5: 0.0010575553057165944 | mAP@0.50:0.95: 0.00028138197011783815\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     72/99     2.676     1.978     5.262     2.748: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.14s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.87s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011\n",
            "Epoch: 72 | mAP@0.5: 0.0021715072697373974 | mAP@0.50:0.95: 0.0008307456030174317\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     73/99     2.741     2.031     5.255     2.663: 100% 4/4 [00:04<00:00,  1.05s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.08s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.85s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.37s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n",
            "Epoch: 73 | mAP@0.5: 0.0007658497298332965 | mAP@0.50:0.95: 0.0002667844203444723\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     74/99     2.737     2.016     5.202      2.71: 100% 4/4 [00:04<00:00,  1.07s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.07s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.13s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.26s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.87s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.37s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n",
            "Epoch: 74 | mAP@0.5: 0.002804220327313575 | mAP@0.50:0.95: 0.0008573694770221916\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     75/99     2.726     1.995     5.193     2.696: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.15s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.07s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.008\n",
            "Epoch: 75 | mAP@0.5: 0.002547520930802475 | mAP@0.50:0.95: 0.0008915343566093971\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     76/99     2.773     2.006     5.175     2.764: 100% 4/4 [00:04<00:00,  1.06s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.10s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.05s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 76 | mAP@0.5: 0.0018508136034399872 | mAP@0.50:0.95: 0.0006714676475958497\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     77/99     2.756     1.997     5.192     2.614: 100% 4/4 [00:04<00:00,  1.05s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.12s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.08s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 77 | mAP@0.5: 0.0026893725562022364 | mAP@0.50:0.95: 0.0009231173957208621\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     78/99     2.708     1.974     5.109      2.68: 100% 4/4 [00:04<00:00,  1.07s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.08s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.40s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.88s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.004\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 78 | mAP@0.5: 0.003849575369068528 | mAP@0.50:0.95: 0.0011330416176916509\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     79/99     2.773     1.989     5.156     2.696: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.18s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.88s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.006\n",
            "Epoch: 79 | mAP@0.5: 0.001792239219006599 | mAP@0.50:0.95: 0.0007388597488008516\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     80/99      2.71     1.989     5.217     2.788: 100% 4/4 [00:04<00:00,  1.04s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.10s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.38s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.91s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 80 | mAP@0.5: 0.0024191482916546114 | mAP@0.50:0.95: 0.0009550677904573214\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     81/99     2.714     1.992     5.198     2.704: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.21s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.90s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 81 | mAP@0.5: 0.002824566532320574 | mAP@0.50:0.95: 0.0009378720988676307\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     82/99     2.746     2.002      5.25     2.774: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.20s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.93s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011\n",
            "Epoch: 82 | mAP@0.5: 0.0028046325531085694 | mAP@0.50:0.95: 0.0007749166276927232\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     83/99     2.826     2.009     5.239     2.749: 100% 4/4 [00:04<00:00,  1.04s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.17s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.90s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.005\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.006\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.010\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.012\n",
            "Epoch: 83 | mAP@0.5: 0.004983682488135256 | mAP@0.50:0.95: 0.00187192184023182\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     84/99     2.745     1.973      5.17     2.728: 100% 4/4 [00:04<00:00,  1.04s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.06s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.38s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.89s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.37s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 84 | mAP@0.5: 0.003129905903839608 | mAP@0.50:0.95: 0.0009654394720239379\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     85/99     2.751     1.982     5.208     2.768: 100% 4/4 [00:04<00:00,  1.04s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.12s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.88s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.37s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 85 | mAP@0.5: 0.002559613519039063 | mAP@0.50:0.95: 0.0009747811853783635\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     86/99     2.738     1.948      5.22     2.755: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.14s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.89s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.004\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 86 | mAP@0.5: 0.004146007623652573 | mAP@0.50:0.95: 0.0013604230654060594\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     87/99     2.779     1.973     5.232     2.777: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.13s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.90s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.009\n",
            "Epoch: 87 | mAP@0.5: 0.003346422762302339 | mAP@0.50:0.95: 0.0008979883867943451\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     88/99     2.821     2.002     5.263     2.698: 100% 4/4 [00:04<00:00,  1.05s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.11s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.23s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.07s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 88 | mAP@0.5: 0.002818659051814291 | mAP@0.50:0.95: 0.0007799934175857097\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     89/99     2.748     1.965     5.223     2.758: 100% 4/4 [00:03<00:00,  1.01it/s]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.09s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.93s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011\n",
            "Epoch: 89 | mAP@0.5: 0.002464946631345105 | mAP@0.50:0.95: 0.0006882788522437844\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     90/99     2.722     1.941     5.105     2.718: 100% 4/4 [00:04<00:00,  1.05s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.16s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.92s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 90 | mAP@0.5: 0.002959344475120238 | mAP@0.50:0.95: 0.0007900364706187752\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     91/99       2.7     1.958     4.979     2.665: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.12s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.01s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.92s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.004\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.007\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 91 | mAP@0.5: 0.003063822019051619 | mAP@0.50:0.95: 0.0009358929419405853\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     92/99     2.702     1.979     5.136      2.72: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.20s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.90s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.004\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.010\n",
            "Epoch: 92 | mAP@0.5: 0.003895490176460533 | mAP@0.50:0.95: 0.0011654527267505052\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     93/99     2.673     1.968     5.219     2.756: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.19s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.23s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.06s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.004\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.012\n",
            "Epoch: 93 | mAP@0.5: 0.0037510587788985895 | mAP@0.50:0.95: 0.0010245456003092454\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     94/99     2.663     1.992     5.112     2.712: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.08s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.38s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.91s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.40s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.004\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.012\n",
            "Epoch: 94 | mAP@0.5: 0.0036380662329819277 | mAP@0.50:0.95: 0.0009167687056423719\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     95/99     2.745     1.995     5.145     2.692: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.17s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.91s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.012\n",
            "Epoch: 95 | mAP@0.5: 0.0026693887157190127 | mAP@0.50:0.95: 0.0007715339972043266\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     96/99     2.757     2.001     5.205     2.714: 100% 4/4 [00:04<00:00,  1.01s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.15s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.90s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.39s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011\n",
            "Epoch: 96 | mAP@0.5: 0.003497939244817715 | mAP@0.50:0.95: 0.000997181915488942\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     97/99      2.76     2.022     5.198     2.734: 100% 4/4 [00:04<00:00,  1.02s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.18s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.25s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.91s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011\n",
            "Epoch: 97 | mAP@0.5: 0.0034934967705090407 | mAP@0.50:0.95: 0.001182990098132355\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     98/99     2.744     1.949     5.143     2.648: 100% 4/4 [00:04<00:00,  1.03s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.17s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.24s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=1.08s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.41s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.002\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.002\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.008\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011\n",
            "Epoch: 98 | mAP@0.5: 0.003286477028324472 | mAP@0.50:0.95: 0.0011104174392147258\n",
            "\n",
            "     Epoch  iou_loss   l1_loss  obj_loss  cls_loss\n",
            "     99/99     2.723     1.984     5.113     2.729: 100% 4/4 [00:04<00:00,  1.04s/it]\n",
            "Inferencing model in val datasets.: 100% 2/2 [00:04<00:00,  2.14s/it]\n",
            "\n",
            "Evaluating speed.\n",
            "\n",
            "Evaluating mAP by pycocotools.\n",
            "Saving runs/train/exp/predictions.json...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "Loading and preparing results...\n",
            "DONE (t=0.39s)\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.91s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.38s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.001\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.003\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.001\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.005\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.003\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.009\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.011\n",
            "Epoch: 99 | mAP@0.5: 0.003454596142400072 | mAP@0.50:0.95: 0.001096205320071562\n",
            "\n",
            "Training completed in 0.230 hours.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tensorboard  (optional)\n",
        "%load_ext tensorboard\n",
        "%tensorboard --logdir runs/train"
      ],
      "metadata": {
        "id": "yFBCgHq_gDmB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Custom Data\n",
        "This guidence explains how to train your own custom data with YOLOv6 (take fine-tuning YOLOv6-s model for example)."
      ],
      "metadata": {
        "id": "EwPPL3Tc0aBF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare your own dataset"
      ],
      "metadata": {
        "id": "4JtfQNUX0-hZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 1** Prepare your own dataset with images. For labeling images, you can use tools like [Labelme](https://github.com/wkentaro/labelme).\n",
        "\n",
        "**Step 2** Generate label files in YOLO format.\n",
        "\n",
        "One image corresponds to one label file, and the label format example is presented as below.\n",
        "\n",
        "```json\n",
        "# class_id center_x center_y bbox_width bbox_height\n",
        "0 0.300926 0.617063 0.601852 0.765873\n",
        "1 0.575 0.319531 0.4 0.551562\n",
        "```\n",
        "\n",
        "- Each row represents one object.\n",
        "- Class id starts from `0`.\n",
        "- Boundingbox coordinates must be in normalized `xywh` format (from 0 - 1). If your boxes are in pixels, divide `center_x` and `bbox_width` by image width, and `center_y` and `bbox_height` by image height.\n",
        "\n",
        "**Step 3** Organize directories.\n",
        "\n",
        "Organize your directory of custom dataset as follows:\n",
        "\n",
        "```shell\n",
        "custom_dataset\n",
        "├── images\n",
        "│   ├── train\n",
        "│   │   ├── train0.jpg\n",
        "│   │   └── train1.jpg\n",
        "│   ├── val\n",
        "│   │   ├── val0.jpg\n",
        "│   │   └── val1.jpg\n",
        "│   └── test\n",
        "│       ├── test0.jpg\n",
        "│       └── test1.jpg\n",
        "└── labels\n",
        "    ├── train\n",
        "    │   ├── train0.txt\n",
        "    │   └── train1.txt\n",
        "    ├── val\n",
        "    │   ├── val0.txt\n",
        "    │   └── val1.txt\n",
        "    └── test\n",
        "        ├── test0.txt\n",
        "        └── test1.txt\n",
        "```\n",
        "\n",
        "**Step 4** Create `dataset.yaml` in `$YOLOv6_DIR/data`.\n",
        "\n",
        "```yaml\n",
        "# Please insure that your custom_dataset are put in same parent dir with YOLOv6_DIR\n",
        "train: ../custom_dataset/images/train # train images\n",
        "val: ../custom_dataset/images/val # val images\n",
        "test: ../custom_dataset/images/test # test images (optional)\n",
        "\n",
        "# whether it is coco dataset, only coco dataset should be set to True.\n",
        "is_coco: False\n",
        "\n",
        "# Classes\n",
        "nc: 20  # number of classes\n",
        "names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',\n",
        "        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']  # class names\n",
        "```"
      ],
      "metadata": {
        "id": "GMPZV_5F0eGQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create a config file"
      ],
      "metadata": {
        "id": "_lthBb8t1ETU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "We use a config file to specify the network structure and training setting, including  optimizer and data augmentation hyperparameters.\n",
        "\n",
        "If you create a new config file, please put it under the configs directory.\n",
        "Or just use the provided config file in `$YOLOV6_HOME/configs/*_finetune.py`.\n",
        "\n",
        "```python\n",
        "## YOLOv6s Model config file\n",
        "model = dict(\n",
        "    type='YOLOv6s',\n",
        "    pretrained='./weights/yolov6s.pt', # download pretrain model from YOLOv6 github if use pretrained model\n",
        "    depth_multiple = 0.33,\n",
        "    width_multiple = 0.50,\n",
        "    ...\n",
        ")\n",
        "solver=dict(\n",
        "    optim='SGD',\n",
        "    lr_scheduler='Cosine',\n",
        "    ...\n",
        ")\n",
        "\n",
        "data_aug = dict(\n",
        "    hsv_h=0.015,\n",
        "    hsv_s=0.7,\n",
        "    hsv_v=0.4,\n",
        "    ...\n",
        ")\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "CqAuhsaQ1J6L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train"
      ],
      "metadata": {
        "id": "rlaEpwIh1b9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python tools/ytrain.py --batch 256 --conf configs/yolov6s_finetune.py --data data/data.yaml"
      ],
      "metadata": {
        "id": "LjyNAANP1o2p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Test Speed"
      ],
      "metadata": {
        "id": "BEa2QWm_nT6S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!python tools/eval.py --data data/coco128.yaml --batch 32 --weights yolov6s.pt --task speed"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9Tb5dlomxch",
        "outputId": "739e27b9-3197-4f84-ba35-85bc5eff4a53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Namespace(batch_size=32, conf_thres=0.001, data='data/coco128.yaml', device='0', half=False, img_size=640, iou_thres=0.65, name='exp', save_dir='runs/val/', task='speed', weights='yolov6s.pt')\n",
            "Loading checkpoint from yolov6s.pt\n",
            "\n",
            "Fusing model...\n",
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n",
            "Switch model to deploy modality.\n",
            "Model Summary: Params: 17.22M, Gflops: 44.19\n",
            "Speed: Checking formats of labels with 2 process(es): \n",
            "128 label(s) found, 0 label(s) missing, 2 label(s) empty, 0 invalid label files: 100% 128/128 [00:00<00:00, 2462.39it/s]\n",
            "Speed: Final numbers of valid images: 128/ labels: 128. \n",
            "0.2s for dataset initialization.\n",
            "Inferencing model in val datasets.: 100% 4/4 [00:01<00:00,  2.24it/s]\n",
            "\n",
            "Evaluating speed.\n",
            "Average pre-process time: 0.16 ms\n",
            "Average inference time: 6.90 ms\n",
            "Average NMS time: 1.36 ms\n",
            "\n",
            "Evaluating mAP by pycocotools.\n"
          ]
        }
      ]
    }
  ]
}